<br /><br />Operational Risk with Excel and VBA<br><br>Preface xiii<br>Acknowledgments xv<br>CHAPTER 1<br>Introduction to Operational Risk Management and Modeling 1<br>What is Operational Risk? 1<br>The Regulatory Environment 3<br>Why a Statistical Approach to Operational Risk Management? 5<br>Summary 6<br>Review Questions 6<br>Further Reading 6<br>CHAPTER 2<br>Random Variables, Risk indicators, and Probability 7<br>Random Variables and Operational Risk Indicators 7<br>Types of Random Variable 8<br>Probability 9<br>Frequency and Subjective Probability 11<br>Probability Functions 13<br>Case Studies 16<br>Case Study 2.1: Downtown Investment Bank 17<br>Case Study 2.2: Mr. Mondey’s OPVaR 20<br>Case Study 2.3: Risk in Software Development 20<br>Useful Excel Functions 24<br>Summary 24<br>Review Questions 25<br>Further Reading 26<br>vii<br>viii CONTENTS<br>CHAPTER 3<br>Expectation, Covariance, Variance, and Correlation 27<br>Expected Value of a RandomVariable 27<br>Variance and Standard Deviation 31<br>Covariance and Correlation 32<br>Some Rules for Correlation, Variance, and Covariance 34<br>Case Studies 35<br>Case Study 3.1: Expected Time to Complete<br>a Complex Transaction 35<br>Case Study 3.2: Operational Cost of System Down Time 37<br>Summary 38<br>Review Questions 38<br>Further Reading 39<br>CHAPTER 4<br>Modeling Central Tendency and Variability of Operational Risk Indicators 41<br>Empirical Measures of Central Tendency 41<br>Measures of Variability 43<br>Case Studies 44<br>Case Study 4.1: Approximating Business Risk 44<br>Excel Functions 47<br>Summary 47<br>Review Questions 48<br>Further Reading 49<br>CHAPTER 5<br>Measuring Skew and Fat Tails of Operational Risk Indicators 51<br>Measuring Skew 51<br>Measuring Fat Tails 54<br>Review of Excel and VBA Functions for Skew and Fat Tails 57<br>Summary 58<br>Review Questions 58<br>Further Reading 58<br>CHAPTER 6<br>Statistical Testing of Operational Risk Parameters 59<br>Objective and Language of Statistical Hypothesis Testing 59<br>Steps Involved In Conducting a Hypothesis Test 61<br>Confidence Intervals 64<br>Case Study 6.1: Stephan’s Mistake 65<br>Excel Functions for Hypothesis Testing 67<br>Contents ix<br>Summary 67<br>Review Questions 68<br>Further Reading 68<br>CHAPTER 7<br>Severity of Loss Probability Models 69<br>Normal Distribution 69<br>Estimation of Parameters 72<br>Beta Distribution 72<br>Erlang Distribution 77<br>Exponential Distribution 77<br>Gamma Distribution 78<br>Lognormal Distribution 80<br>Pareto Distribution 81<br>Weibull Distribution 81<br>Other Probability Distributions 83<br>What Distribution Best Fits My Severity of Loss Data? 84<br>Case Study 7.1: Modeling Severity of Loss Legal<br>Liability Losses 86<br>Summary 91<br>Review Questions 91<br>Further Reading 92<br>CHAPTER 8<br>Frequency of Loss Probability Models 93<br>Popular Frequency of Loss Probability Models 93<br>Other Frequency of Loss Distributions 98<br>Chi-Squared Goodness of Fit Test 100<br>Case Study 8.1: Key Personnel Risk 102<br>Summary 103<br>Review Questions 103<br>Further Reading 103<br>CHAPTER 9<br>Modeling Aggregate Loss Distributions 105<br>Aggregating Severity of Loss and Frequency<br>of Loss Distributions 105<br>Calculating OpVaR 108<br>Coherent Risk Measures 110<br>Summary 112<br>Review Questions 112<br>Further Reading 112<br>x CONTENTS<br>CHAPTER 10<br>The Law of Significant Digits and Fraud Risk Identification 113<br>The Law of Significant Digits 113<br>Benford’s Law in Finance 116<br>Case Study 10.1: Analysis of Trader’s Profit and Loss<br>Using Benford’s Law 116<br>A Step Towards Better Statistical Methods of Fraud Detection 118<br>Summary 120<br>Review Questions 120<br>Further Reading 120<br>CHAPTER 11<br>Correlation and Dependence 121<br>Measuring Correlation 121<br>Dependence 132<br>Stochastic Dependence 134<br>Summary 136<br>Review Questions 136<br>Further Reading 136<br>CHAPTER 12<br>Linear Regression in Operational Risk Management 137<br>The Simple Linear Regression Model 137<br>Multiple Regression 148<br>Prediction 153<br>Polynomial and Other Types of Regression 155<br>Multivariate Multiple Regression 155<br>Regime-Switching Regression 157<br>The Difference Between Correlation and Regression 158<br>A Strategy for Regression Model Building<br>in Operational Risk Management 159<br>Summary 159<br>Review Questions 159<br>Further Reading 160<br>CHAPTER 13<br>Logistic Regression in Operational Risk Management 161<br>Binary Logistic Regression 161<br>Bivariate Logistic Regression 165<br>Case Study 13.1: Nostro Breaks and Volume<br>in a Bivariate Logistic Regression 172<br>Other Approaches for Modeling Bivariate Binary Endpoints 173<br>Contents xi<br>Summary 176<br>Review Questions 177<br>Further Reading 177<br>CHAPTER 14<br>Mixed Dependent Variable Modeling 179<br>A Model for Mixed Dependent Variables 179<br>Working Assumption of Independence 181<br>Understanding the Benefits of Using a WAI 184<br>Case Study 14.1: Modeling Failure in Compliance 184<br>Summary 185<br>Review Questions 186<br>Further Reading 186<br>CHAPTER 15<br>Validating Operational Risk Proxies Using Surrogate Endpoints 187<br>The Need for Surrogate Endpoints in OR Modeling 187<br>The Prentice Criterion 188<br>Limitations of the Prentice Criterion 191<br>The Real Value Added of Using Surrogate Variables 193<br>Validation Via the Proportion Explained 196<br>Limitations of Surrogate Modelling in Operational<br>Risk Management 200<br>Case Study 15.1: Legal Experience as a Surrogate Endpoint<br>for Legal Costs for a Business Unit 201<br>Summary 202<br>Review Questions 202<br>Further Reading 202<br>CHAPTER 16<br>Introduction to Extreme Value Theory 203<br>Fisher-Tippet–Gnedenko Theorem 203<br>Method of Block Maxima 205<br>Peaks over Threshold Modeling 206<br>Summary 207<br>Review Questions 207<br>Further Reading 207<br>CHAPTER 17<br>Managing Operational Risk with Bayesian Belief Networks 209<br>What is a Bayesian Belief Network? 209<br>Case Study 17.1: A BBN Model for Software Product Risk 212<br>Creating a BBN-Based Simulation 215<br>xii CONTENTS<br>Assessing the Impact of Different Managerial Strategies 216<br>Perceived Benefits of Bayesian Belief Network Modeling 218<br>Common Myths About BBNs—<br>The Truth for Operational Risk Management 222<br>Summary 224<br>Review Questions 224<br>Further Reading 224<br>CHAPTER 18<br>Epilogue 225<br>Winning the Operational Risk Argument 225<br>Final Tips on Applied Operational Risk Modeling 226<br>Further Reading 226<br>Appendix<br>Statistical Tables 227<br>Cumulative Distribution Function of the Standard<br>Normal Distribution 227<br>Chi-Squared Distribution 230<br>Student’s t Distribution 232<br>F Distribution 233<br>Notes 237<br>Bibliography 245<br>About the CD-ROM 255<br>Index 259<br><br>
<p align="right"><font color="#000066">[此贴子已经被作者于2009-1-24 1:08:58编辑过]</font></p><br>yinjb
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&nbsp;奖励&nbsp;2009-2-7 17:49:15